دانلود کتاب Regression analysis with Python : learn the art of regression analysis with Python

فهرست مطالب :

Content: Cover
Copyright
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Table of Contents
Preface
Chapter 1: Regression --
The Workhorse of Data Science
Regression analysis and data science
Exploring the promise of data science
The challenge
The linear models
What you are going to find in the book
Python for data science
Installing Python
Choosing between Python 2 and Python 3
Step-by-step installation
Installing packages
Package upgrades
Scientific distributions
Introducing Jupyter or IPython
Python packages and functions for linear models
NumPy
SciPy StatsmodelsScikit-learn
Summary
Chapter 2: Approaching Simple Linear Regression
Defining a regression problem
Linear models and supervised learning
Reflecting on predictive variables
Reflecting on response variables
The family of linear models
Preparing to discover simple linear regression
Starting from the basics
A measure of linear relationship
Extending to linear regression
Regressing with StatsModels
The coefficient of determination
Meaning and significance of coefficients
Evaluating the fitted values
Correlation is not causation
Predicting with a regression model Regressing with Scikit-learnMinimizing the cost function
Explaining the reason for using squared errors
Pseudoinverse and other optimization methods
Gradient Descent at work
Summary
Chapter 3: Multiple Regression in Action
Using multiple features
Model building with Statsmodels
Using formulas as an alternative
The correlation matrix
Revisiting gradient descent
Feature scaling
Unstandardizing coefficients
Estimating feature importance
Inspecting standardized coefficients
Comparing models by R-squared
Interaction models
Discovering interactions
Polynomial regression Testing linear versus cubic transformationGoing for higher-degree solutions
Introducing underfitting and overfitting
Summary
Chapter 4: Logistic Regression
Defining a classification problem
Formalization of the problem: binary classification
Assessing the classifier's performance
Defining a probability-based approach
More on the logistic and logit functions
Let's see some code
Pros and cons of logistic regression
Revisiting Gradient Descend
Multiclass Logistic Regression
An example
Summary
Chapter 5: Data Preparation
Numeric feature scaling
Mean centering
Standardization NormalizationThe logistic regression case
Qualitative feature encoding
Dummy coding with Pandas
DictVectorizer and one-hot encoding
Feature hasher
Numeric feature transformation
Observing residuals
Summarizations by binning
Missing data
Missing data imputation
Keeping track of missing values
Outliers
Outliers on the response
Outliers among the predictors
Removing or replacing outliers
Summary
Chapter 6: Achieving Generalization
Checking on out-of-sample data
Testing by sample split
Cross-validation
Bootstrapping
Greedy selection of features
The Madelon dataset